Add 13 new procedural datasets across 7 categories

New dataset categories: combinatorics, statistics, optimization, and
formal languages. Extended existing algebra, arithmetic, probability,
logic, and graphs packages with complex_advanced, linear_algebra, limits,
number_theory, conditional_probability, set_operations, and job_scheduling.

Each dataset includes config validation, deterministic seeding, custom
scoring, curriculum support, and comprehensive unit tests (92 new tests).

tests/test_linear_programming.py

@@ -0,0 +1,65 @@
+import pytest
+
+from reasoning_gym.optimization.linear_programming import (
+    LinearProgrammingConfig,
+    LinearProgrammingCurriculum,
+    LinearProgrammingDataset,
+)
+
+
+def test_config_validation():
+    with pytest.raises(AssertionError):
+        config = LinearProgrammingConfig(min_coeff=0)
+        config.validate()
+
+    with pytest.raises(AssertionError):
+        config = LinearProgrammingConfig(num_constraints=1)
+        config.validate()
+
+
+def test_deterministic():
+    config = LinearProgrammingConfig(seed=42, size=10)
+    ds1 = LinearProgrammingDataset(config)
+    ds2 = LinearProgrammingDataset(config)
+    for i in range(len(ds1)):
+        assert ds1[i] == ds2[i]
+
+
+def test_item_structure():
+    config = LinearProgrammingConfig(seed=42, size=50)
+    ds = LinearProgrammingDataset(config)
+    for i in range(len(ds)):
+        item = ds[i]
+        assert isinstance(item, dict)
+        assert "question" in item
+        assert "answer" in item
+        assert "metadata" in item
+        assert item["metadata"]["source_dataset"] == "linear_programming"
+
+
+def test_answer_correctness():
+    config = LinearProgrammingConfig(seed=42, size=50)
+    ds = LinearProgrammingDataset(config)
+    for i in range(len(ds)):
+        item = ds[i]
+        score = ds.score_answer(item["answer"], item)
+        assert score >= 1.0, f"Item {i}: oracle answer scored {score}"
+
+
+def test_score_wrong_answer():
+    config = LinearProgrammingConfig(seed=42, size=10)
+    ds = LinearProgrammingDataset(config)
+    item = ds[0]
+    assert ds.score_answer(None, item) == 0.0
+    assert ds.score_answer("not a number", item) == 0.0
+
+
+def test_curriculum():
+    curriculum = LinearProgrammingCurriculum()
+    base_value = {"size": 50, "seed": 1}
+    base_cfg = curriculum.generate_configuration(base_value)
+    assert base_cfg.seed == 1
+
+    curriculum.increment_attr_level("num_constraints")
+    increased_cfg = curriculum.generate_configuration(base_value)
+    assert increased_cfg.num_constraints >= base_cfg.num_constraints